{"id":"W4220864256","doi":"10.2166/wst.2022.095","title":"An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methods","year":2022,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; CentrEau - Quebec Water Management Research Centre","funders":"","keywords":"Generator (circuit theory); Water quality; Range (aeronautics); Inlet; Automation; Data mining; Computer science; Multivariate statistics; Volumetric flow rate; Artificial neural network; Engineering; Environmental science; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006338179,0.0009460332,0.0007046185,0.000563616,0.0003456431,0.0009665433,0.001425554,0.001032468,0.002986505],"category_scores_gemma":[0.001329554,0.0004222734,0.0009404944,0.0005656562,0.0003674091,0.001025727,0.0007532918,0.001606298,0.001089124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006354525,"about_ca_system_score_gemma":0.001077318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009773562,"about_ca_topic_score_gemma":0.005966776,"domain_scores_codex":[0.9997097,0.00007107462,0.00002300571,0.00007209962,0.0001037906,0.00002032971],"domain_scores_gemma":[0.9996383,0.0001252557,0.0000428097,0.00008187866,0.00009644675,0.00001531589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000239307,0.00003698212,0.0007433933,0.0001079837,0.00003109841,0.0000712461,0.00004724062,0.9642531,0.003533543,0.008107452,0.001350976,0.02169304],"study_design_scores_gemma":[0.000005488862,0.00001242369,0.0001471468,0.00001033771,0.000004541617,0.00001383638,0.000005338579,0.9916179,0.0012745,0.002361023,0.004538109,0.000009348692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007762059,0.00009525363,0.9856125,0.0001554716,0.0000596161,0.00009718506,0.001285263,0.001605911,0.003326731],"genre_scores_gemma":[0.4752849,0.0005281235,0.5101059,0.0001699957,0.00009194584,0.0008673134,0.004138033,0.0008288221,0.007984981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009773562,"threshold_uncertainty_score":0.01943332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08617119338889614,"score_gpt":0.3536946479374413,"score_spread":0.2675234545485452,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}